An Efficient Machine Learning-based Channel Prediction Technique for OFDM Sub-Bands

TL;DR

This paper introduces a machine learning-based method for predicting channels in OFDM sub-bands, aiming to improve wireless communication performance by accurately estimating time-varying channel conditions.

Contribution

It presents a novel ML technique trained on channel fading samples to predict future channel behavior in OFDM sub-bands, addressing the complexity of wireless environments.

Findings

Enhanced channel prediction accuracy demonstrated

Reduced computational complexity compared to traditional methods

Improved performance in dynamic wireless scenarios

Abstract

The acquisition of accurate channel state information (CSI) is of utmost importance since it provides performance improvement of wireless communication systems. However, acquiring accurate CSI, which can be done through channel estimation or channel prediction, is an intricate task due to the complexity of the time-varying and frequency selectivity of the wireless environment. To this end, we propose an efficient machine learning (ML)-based technique for channel prediction in orthogonal frequency-division multiplexing (OFDM) sub-bands. The novelty of the proposed approach lies in the training of channel fading samples used to estimate future channel behaviour in selective fading.